Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Diagnostic classification of undifferentiated fevers using artificial neural network
    (American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.
    Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).
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    Optimized diet plan using unbounded knapsack Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2020) Bobade, P.; Kumar, P.; Chandrasekaran, K.; Divakarla, D.
    Cholesterol, hypertension and diabetes are the three major chronic diseases from which most of the people suffers and these peoples often use search engines to acquire related information about these problems. But, almost every information related to diet on the internet isn't suitable for people to gather information about the diet suggestions. A system for diet suggestion which can advocate a prudent diet for such peoples is suggested in this paper. We designed a system that recommends a proper diet which has the adequate knowledge of three above mentioned highly chronic diseases. We propose a solution to the menu recommending problem using the optimization algorithm known as unbounded knapsack. We designed a model which satisfies the nutritional requirements of individuals while imposing the 'Laws of Nutrition', a set of hypothesis used by almost all Latin America's nutrition scientists. This prototype corresponds to a numerical optimization problem with constraints. We design a menu items generator application model to set up a convenient menu for a user with different properties. © 2020 IEEE.
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    A Deep Learning Framework for Plant Disease Detection
    (Springer Science and Business Media Deutschland GmbH, 2025) Munda, K.K.; Patil, N.
    As a major source of nutritious food, the agriculture industry supports economies and feeds people. Yet, the production of food is severely hampered by plant diseases. Major crops like wheat (21.5%), rice (30.0%), maize (22.6%), potatoes (17.2%), and soybeans (21.4%) have significant annual output declines due to numerous diseases, according to recent studies. Since deep learning technologies have been developed, image categorization accuracy has increased dramatically. Using CNN and vision transformer models, we examine the Plant Village dataset in this study, which consists of 54,305 sample images that illustrate various plant disease species in 38 classifications. Using a focus on potato leaves and a total of 2151 samples, we evaluate the model’s performance in comparison to other models in terms of training and testing accuracy, and we obtained impressive results. The models’ respective training accuracy is 97.27% for the CNN and 94.7% for the ViT model, while their validation accuracy is 100% and 94.27%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.